Specimens are sliced and imaged for analysis purposes as well as teaching purposes. With a stack of images that describe a sliced specimen one can create synthetic views of that specimen to analyze the structure and anatomy of the specimen. After cutting the specimen in the lab e.g. from a paraffin block, the common coordinate system of the sliced specimen is lost which causes misalignment in the images of the stack. This loss results in translational and rotational differences between the images of the stack. Another misalignment source can come from the slice preparation which is based on manually performed steps. Translation, scaling and stretching can occur. Furthermore, deformations such as stretched tissue, broken tissue and absent tissue can be found.
To align the histology slices of the specimen in the digital domain (i.e. after imaging) it is required to counteract the results of the deformation process as well as the loss of the common coordinate system. A typical solution for alignment is to add markers as artificial fiducials in the specimen block during preparation, e.g. by embedding markers in the paraffin, that are detected after imaging. The positions of these markers can then be used for calculating transformation parameters. However, such additional markers results in an additional preparation step. Furthermore, the storage space for storing the images after digitization increases because additional area is imaged for capturing the markers that are added alongside the real specimen. Therefore, it is desirable to align an ordered stack of images without usage of additional markers in the specimen block.
The article “Computer-Based Alignment and Reconstruction of Serial Sections”, John C. Fiala and Kirsten M. Harris, Boston University, Microscopy and Analysis, pages 5-7, January 2002, describes a way to align an series of images without having to add additional markers in the specimen block. For the alignment, images are transformed by computation from a set of point correspondences entered by the user. For aligning the series of images, an alignment process is repeated, wherein an image to be aligned next is aligned with the image aligned immediately before.
Additionally to the above cited reference, further references are mentioned in the following paragraphs:                Gerard de Haan et al, “True motion estimation with 3D-recursive search block matching”, IEEE transactions on circuits and systems of video technology, volume 3, number 5, October 1993;        U.S. Pat. No. 5,072,293;        US2008/0144716A1;        David G. Lowe, “Object recognition from local scale-invariant features,” International Conference on Computer Vision, 1999, pages 1150-1157; and        David G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 2004, pages 91-110.        